{
  "abstract": "Evaluation of lymph node (LN) metastasis is critical for cancer staging and treatment planning. Since radiologic hallmarks of metastatic LNs are shared across primary cancers, a computed tomography (CT)-based deep learning (DL) classifier developed from one anatomy may transfer to others. This study built a DenseNet-121 ensemble model using clinical-implication-applied preprocessing and evaluated its cross-anatomical generalizability to LNs from diverse primary cancers. A DenseNet-121 ensemble model combining fixed-size cropping (FSC) and adjustable square cropping (ASC) was trained from ImageNet initialization on an axillary lymph node (ALN) dataset (n = 1,128 from 523 breast cancer patients). The model utilized clinical-implication-applied perinodal cropping with bicubic filtering and standardized resizing. To test cross-anatomical generalizability, an external dataset of 256 CT images across four anatomical regions was evaluated: cervical, mediastinal, upper-abdominal, and lower-abdominal LNs. Perinodal free margins were systematically varied from 10% to 40% to optimize the balance between context preservation and surround interference. Bicubic filtering and standardized resizing yielded the highest internal performance on the original ALN dataset (FSC-ASC ensemble area under the receiver operating characteristic curve [AUROC] 0.958, 95% confidence interval [CI] 0.920-0.987 at 40% margin). The model demonstrated site-dependent, above-chance generalizability across all external regions. Cervical LNs achieved the highest performance at a 40% margin (AUROC 0.939, 95% CI 0.875-0.989). Mediastinal LNs peaked at a 10% margin (AUROC 0.796, 95% CI 0.672-0.903), with wider margins degrading performance due to adjacent structural interference. Upper-abdominal and lower-abdominal LNs reached optimal performance at a 30% margin (AUROC 0.814 for upper; AUROC 0.917 for lower). A single ALN-trained DL model can accurately predict LN metastasis across cervical, mediastinal, and abdominal stations through site-specific perinodal margin tuning. These findings support the feasibility of cross-cancer knowledge transfer and the clinical utility of generalizable backbones for pan-cancer nodal assessment.",
  "abstract_shown": true,
  "affiliations": [
    "Hallym University Sacred Heart Hospital"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "J. Kang"
    },
    {
      "name": "J. W. Kang"
    },
    {
      "name": "L. M. Kwon"
    },
    {
      "name": "B.-J. Cho"
    },
    {
      "affs": [
        0
      ],
      "name": "B. J. Kim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cb7zheq3fewyd.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.04.764191",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764191v1.full",
  "id": "cb7zheq3fewyd",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764191v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764191v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cb7zheq3fewyd.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:22:03Z",
      "native_id": "10.64898/2026.11.04.764191",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.04.764191v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Cross-cancer knowledge transfer for lymph node metastasis prediction: Applicability of an axillary lymph node-based deep learning model",
  "title_text": "Cross-cancer knowledge transfer for lymph node metastasis prediction: Applicability of an axillary lymph node-based deep learning model",
  "type": "preprint",
  "updated_at": "2026-11-06T00:21:45Z",
  "url": "https://research.draconitic.com/paper/cb7zheq3fewyd",
  "venue": "bioRxiv",
  "versions": []
}